Energy Efficient Cluster Head Selection using Hybrid Sandpiper Harmony Search Algorithm Optimization in IoT based Sensor Networks

Abstract

In Wireless Sensor Networks, the selections of Cluster Heads are an important task in preserving energy consumption and sustaining
long-term communication performance. Earlier meta- heuristic approaches, including the combination of Sandpiper Optimization and Competitive
Swarm Optimization (SOA–CSO), have shown improvements in the initial stability period, but it has uneven load distribution. To overcome this
limitation, this research work hybrid Sandpiper Optimization Algorithm - Harmony Search Algorithm (SOA–HSA) introduced an effective Cluster
Head selection. This proposed method uses SOA to explore the search space widely and identify best CH candidates, while Harmony Search refines
these candidates by adjusting cluster formation, communication paths, and energy allocation. This two-level optimization helps in reducing
unnecessary transmissions, balancing the number of nodes under each CH, and minimizing the communication distance, which collectively
enhances the overall network performance. The experimental results confirm that the SOA–HSA model achieves longer stability, increased lastnode survival, and improved throughput when compared with the other algorithm.